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where Pa denotes the prior covariance of the accelerometer bias xa , Hg = h h g , Hm = m , and Pm is the covariance of the averaged magnetometer noise over the initialization period Also as described in Exercise 411, the covariance of the initial condition error vector [ , xa ] is PE = T Hg (Pa + Pf )Hg + Hm Pm Hm T Pa Hg T T Hg Pa Pa

15:

where T is de ned in eqn (280) If we de ne P P a = = T Hg (Pa + Pf )Hg + Hm Pm Hm T T Hg Pa = Pa ,

Now imagine that it is an evening in the middle of October, a couple of hours after sunset, and you are at the latitude of Lake Tahoe, Indianapolis, or Washington, DC (approximately 39 N) New constellations have risen in the east, and old ones set in the west The sky is looked after by new custodians as the nights grow longer Here are constellations we have not described before that now occupy prominent positions in the sky

FIGURE 15-30

then the initial covariance matrix for the error state x is P 0 P a Pgg 0 P+ (t2 ) = P (t2 ) = 0 Paa Pa 0

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window or in Context Explorer, you can view the cardinality in one of two different notations Merise notation uses the minmax cardinality format like 11 and 0n to mean 1 and only 1 and zero or more, respectively Merise notation is good at explicitly conveying the cardinality However, when your model starts to get complex with lots of joins, and when you are trying to get the big picture to identify facts with the many side of the relationships versus dimensional tables with the one side of the relationship, Merise notation starts to lose its value It requires reading the values to understand the cardinality, and sometimes the values get obliterated in the diagram when many relationships are packed together In this case, we recommend switching to Crowsfeet notation, which is shown in Figure 15-30 The graphical format of the many side of the relationship is easy to pick up, as is the circle on the relationship indicating an optional (outer join) relationship To switch the cardinality notation, choose Diagram | Diagram Settings and select the appropriate radio button On versions prior to 83, the menu selection Diagram | Notation

(1066)

High in the southeast you will see Pisces, the two fish, and Aries, the winged ram (Fig 2-22) Legend has it that Pisces were joined or tied together at their tails long ago, and to this day they are flailing about in that unfortunate condition Aries has fleece of gold, and for this reason, the ram is sought after by a cosmic spirit called Jason and his cohorts called the Argonauts

Part IV:

Using either approach of Section 103, the initial values for the quaternion b(t2 ), the gyro errors xg (t2 ), and the accelerometer errors xa (t2 ) can be de ned from the sensor data Given these initial conditions, the mechanization equations of Section 104 would be integrated through time to provide the reference trajectory b(t) for t > t2 In addition, at any time t that they are required, the quaternion can be used to compute values for either

Creating a good model that produces valid results requires that you understand how to address modeling challenges As mentioned in the section How Cognos 8 Uses Relationships to Generate Queries, your relationships and cardinality settings dictate how queries are put together If you are modeling a star schema data warehouse, relationships are straightforward, linking fact tables on the many side to dimension tables on the one side Challenges arise when a straightforward modeling approach used on a normalized database results in ambiguity or incorrect joins The cardinality of a relationship might not correctly indicate a fact table or a reference table To remove ambiguities or traps, as Cognos refers to them, you might need to use model query subjects to make your model appear as if it was created on a star schema data warehouse That will allow Cognos 8 to correctly identify fact query subjects and produce predictable queries You also might need to alias query subjects to solve other challenges

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